RABIT

RABIT integrates ENCODE ChIP-seq profiles and TCGA tumor-profiling data to identify transcription factor regulatory effects on gene expression in cancers while controlling for background genomic confounders such as copy number alterations and DNA methylation.

Key Features:

• Data integration: Integrates 686 ENCODE ChIP-seq profiles representing 150 transcription factors with 7484 TCGA tumor datasets across 18 cancer types.
• Background effect control: Tests whether TF target genes exhibit significant differential regulation while controlling for copy number alterations and DNA methylation.
• Profile prioritization: Prioritizes the most relevant ChIP-seq profile for a given TF in each tumor context when multiple profiles are available.
• Correlation analysis: Assesses correlations between TF expression levels, somatic mutation variations, and differential expression patterns of target genes within each cancer type.
• Validation against databases: Produces predictions that are consistent with cancer-related gene databases.
• Extension to RNA-binding proteins: Applies the same framework to RNA-binding protein motifs to reveal effects of alternative splicing factors on target gene 3'UTRs.

Scientific Applications:

• Predicting oncogenic regulators: Predicts oncogenic roles of transcription factors and other gene expression regulators in cancer.
• Systematic identification across cancers: Identifies cancer-associated TFs and regulatory elements across 18 cancer types using integrated ChIP-seq and TCGA data.
• Target discovery and tumor biology: Facilitates discovery of novel therapeutic targets and elucidation of transcriptional mechanisms underlying tumor development.
• Precision oncology support: Links genetic alterations and transcriptional regulation to inform precision medicine hypotheses for individual tumor profiles.

Methodology:

Performs regression analysis with background integration on 686 ENCODE ChIP-seq profiles (150 TFs) and 7484 TCGA tumor samples across 18 cancer types; tests TF target differential regulation controlling for copy number alterations and DNA methylation; prioritizes ChIP-seq profiles per tumor context; assesses correlations among TF expression, somatic mutation variations, and target gene differential expression; extends analysis to RNA-binding protein motifs and 3'UTR interactions.